The testing episodes are constructed by pairing each query input with each support input, resulting in \(N\times N\)  comparison pairs for each episode. Each query example is assigned a label of the support example with the highest score \(s\) .
The model accuracy is defined as the fraction of the labels correctly assigned.
To compare our model with ARC [1]}, which is the previous state of the art for the task, we also experimented in the scenario assumed by ARC when the query examples in one episode are known to belong to different classes. We used Hungarian matching [2]} which assigns each support example to a unique query example in the episode, while maximizing overall score.
